Authors : R. Sandhiya; Dr. K. Priya

Volume/Issue : Volume 11 - 2026, Issue 10 - October

Google Scholar : https://tinyurl.com/t7jfh842

DOI : https://doi.org/10.38124/ijisrt/26Oct570

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The rapid growth of digital banking services has increased the convenience of online financial transactions. However, it has also exposed banking systems to emerging cyber threats such as phishing, ransomware, Distributed Denialof-Service (DDoS) attacks, account takeover, and suspicious transaction activities. Traditional rule-based security systems may struggle to identify previously unseen attacks and adapt to changing threat patterns. This study proposes CyberSentinel-DB, a machine learning-based framework for detecting and preventing emerging cyber threats in digital banking. The proposed framework combines supervised machine learning techniques for classifying known cyber threats with unsupervised learning techniques for identifying unusual activities and potential unknown attacks. Transactional patterns, login behavior, IP addresses, transaction frequency, and session-related features can be analyzed to identify suspicious activities. The framework also considers real-time threat monitoring, automated alerts, and preventive actions to improve banking security. Model performance can be evaluated using accuracy, precision, recall, F1-score, false-positive rate, and detection latency. The proposed framework aims to support adaptive, scalable, and privacy-aware cyber threat detection in digital banking environments.

Keywords : CyberSentinel-DB, Digital Banking Security, Machine Learning, Cyber Threat Detection, Anomaly Detection, Phishing Detection, Ransomware, DDoS Attacks.

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